Adoption of Mobile Apps for Depression and Anxiety: Cross-Sectional Survey Study on Patient Interest and Barriers to Engagement

JMIR Mental Health, Journal Year: 2018, Volume and Issue: 6(1), P. e11334 - e11334

Published: Sept. 8, 2018

Emerging research suggests that mobile apps can be used to effectively treat common mental illnesses like depression and anxiety. Despite promising efficacy results ease of access these interventions, adoption health (mHealth; device-delivered) interventions for illness has been limited. More insight into patients' perspectives on mHealth is required create effective implementation strategies adapt existing facilitate higher rates adoption.The aim this study was examine, from the patient perspective, current use factors may impact illness.This a cross-sectional survey veterans who had attended an appointment at single Veterans Health Administration facility in early 2016 associated with one following concerns: unipolar depression, any anxiety disorder, or posttraumatic stress disorder. We Veteran Affairs Corporate Data Warehouse subsets eligible participants demographically stratified by gender (male female) minority status (white nonwhite). From each subset, 100 were selected random mailed paper items addressing demographics, overall health, technology ownership use, interest app illness, reasons nonuse, specific features illness.Of 400 potential participants, 149 (37.3%, 149/400) completed returned survey. Most (79.9%, 119/149) reported they owned smart device general (71.1%, 106/149). (73.1%, 87/149) using but only 10.7% (16/149) done so. Paired samples t tests indicated ratings recommended clinician significantly greater than even when recommending specialty provider. The most frequent concerns related lacking proof (71.8%, 107/149), about data privacy (59.1%, 88/149), not knowing where find such (51.0%, 76/149). Participants expressed number particularly high-interest context-sensitive (85.2%, 127/149), focused areas: increasing exercise (75.8%, 113/149), improving sleep (73.2%, 109/149), changing negative thinking (70.5%, 105/149), involvement activities (67.1%, 100/149).Most respondents devices already other purposes, interested illness. Key improve include provider endorsement, publicity efficacious apps, clear messaging information. Finally, multifaceted address range concerns, thought patterns, best received.

Language: Английский

---

Psychiatric diagnosis and treatment in the 21st century: paradigm shifts versus incremental integration

World Psychiatry, Journal Year: 2022, Volume and Issue: 21(3), P. 393 - 414

Published: Sept. 8, 2022

Psychiatry has always been characterized by a range of different models and approaches to mental disorder, which have sometimes brought progress in clinical practice, but often also accompanied critique from within without the field. Psychiatric nosology particular focus debate recent decades; successive editions DSM ICD strongly influenced both psychiatric practice research, led assertions that psychiatry is crisis, advocacy for entirely new paradigms diagnosis assessment. When thinking about etiology, many researchers currently refer biopsychosocial model, this approach received significant critique, being considered some observers overly eclectic vague. Despite development evidence-based pharmacotherapies psychotherapies, current evidence points treatment gap research-practice health. In paper, after considering we discuss proposed novel perspectives recently achieved prominence may significantly impact research future: neuroscience personalized pharmacotherapy; statistical nosology, assessment research; deinstitutionalization community health care; scale-up psychotherapy; digital phenotyping therapies; global task-sharing approaches. We consider extent transitions practices reflect hype or hope. Our review indicates each contributes important insights allow hope future, provides only partial view, any promise paradigm shift field not well grounded. conclude there crucial advances that, despite progress, considerable need further improvements intervention; such will likely be specific shifts rather incremental iterative integration.

Language: Английский

---

Reviewing the data security and privacy policies of mobile apps for depression

Internet Interventions, Journal Year: 2018, Volume and Issue: 15, P. 110 - 115

Published: Dec. 20, 2018

Mobile apps have become popular resources for mental health support. Availability of information about developers' data security procedures apps, specifically those targeting health, has not been thoroughly investigated. If people are to use and trust these tools their it is crucial we evaluate the transparency quality around practices apps. The present study reviewed privacy policies mobile depression.We retrieved from iTunes Google Play stores in October 2017, using term "depression", evaluated handling apps.We identified 116 eligible phone Of those, 4% (5/116) received a score acceptable, 28% (32/116) questionable, 68% (79/116) unacceptable. Only minority (49%) had policy. availability differed significantly by platform, with more likely policy than store. collecting identifiable were (79%) compared only non-identifiable (34%).The majority sufficiently transparent regarding security. Apps great potential scale resources, providing unable or reluctant access traditional face-to-face care, as an adjunct treatment. However, if they be reasonable resource, must safe, secure, responsible.

Language: Английский

---

Conversational Artificial Intelligence in Psychotherapy: A New Therapeutic Tool or Agent?

The American Journal of Bioethics, Journal Year: 2022, Volume and Issue: 23(5), P. 4 - 13

Published: April 1, 2022

Conversational artificial intelligence (CAI) presents many opportunities in the psychotherapeutic landscape—such as therapeutic support for people with mental health problems and without access to care. The adoption of CAI poses risks that need in-depth ethical scrutiny. objective this paper is complement current research on ethics AI by proposing a holistic, ethical, epistemic analysis adoption. First, we focus question whether rather tool or an agent. This serves framework subsequent focusing topics (self-) knowledge, (self-)understanding, relationships. Second, propose further conceptual regarding human-AI interaction argue cannot be considered equal partner conversation case human therapist. Instead, CAI's role should restricted specific functions.

Language: Английский

---

Digital health competencies in medical school education: a scoping review and Delphi method study

BMC Medical Education, Journal Year: 2022, Volume and Issue: 22(1)

Published: Feb. 26, 2022

Abstract Introduction In order to fulfill the enormous potential of digital health in healthcare sector, must become an integrated part medical education. We aimed investigate which knowledge, skills and attitudes should be included a curriculum for students through scoping review Delphi method study. Methods conducted literature on relevant Key topics were split into three sub-categories: knowledge (facts, concepts, information), (ability carry out tasks) (ways thinking or feeling). Thereafter, we used modified where experts rated over two rounds based whether scale from 1 (strongly disagree) 5 agree). A predefined cut-off ≥4 was identify that critical include students. Results The resulted total 113 articles, with 65 extracted questionnaire. by 18 experts, all completed both questionnaire rounds. 40 (62%) across sub-categories met rating value ≥4. Conclusion An expert panel identified important within skills, taught. These can help guide educators development future curricula.

Language: Английский

---

Systematic Review of Digital Phenotyping and Machine Learning in Psychosis Spectrum Illnesses

Harvard Review of Psychiatry, Journal Year: 2020, Volume and Issue: 28(5), P. 296 - 304

Published: Aug. 12, 2020

Abstract Background Digital phenotyping is the use of data from smartphones and wearables collected in situ for capturing a digital expression human behaviors. techniques can be used to analyze both passively (e.g., sensor) actively survey) data. Machine learning offers possible predictive bridge between future clinical state. This review examines passive across schizophrenia spectrum bipolar disorders, with focus on machine-learning studies. Methods A systematic literature was conducted using keywords related severe mental illnesses, data-collection devices smartphones, wearables, actigraphy devices), streams collected. Searches five databases initially yielded 3312 unique publications. Fifty-one studies were selected inclusion, 16 techniques. Results All differed features used, pre-processing, analytical techniques, algorithms tested, performance metrics reported. Across all studies, other study factors reported also varied widely. Machine-learning focused random forest, support vector, neural net approaches, almost exclusively disorder. Discussion Many have been applied Larger improved quality, are needed, as further research application machine early diagnosis treatment psychosis. In order achieve greater comparability common elements identified inclusion

Language: Английский

---

Digital Clinics and Mobile Technology Implementation for Mental Health Care

Current Psychiatry Reports, Journal Year: 2021, Volume and Issue: 23(7)

Published: May 7, 2021

Language: Английский

---

Implementation of Cognitive Behavioral Therapy in e–Mental Health Apps: Literature Review

Journal of Medical Internet Research, Journal Year: 2021, Volume and Issue: 24(3), P. e27791 - e27791

Published: Dec. 28, 2021

Background To address the matter of limited resources for treating individuals with mental disorders, e–mental health has gained interest in recent years. More specifically, mobile (mHealth) apps have been suggested as electronic interventions accompanied by cognitive behavioral therapy (CBT). Objective This study aims to identify therapeutic aspects CBT that implemented existing mHealth and technologies used. From these, we aim derive research gaps should be addressed future. Methods Three databases were screened studies on context disorders implement techniques CBT: PubMed, IEEE Xplore, ACM Digital Library. The independently selected 2 reviewers, who then extracted data from included studies. Data their technical implementation synthesized narratively. Results Of 530 retrieved citations, 34 (6.4%) this review. exploit two groups technologies: restructuring, activation, problem solving (exposure is not yet realized apps) increase user experience, adherence, engagement. synergy these enables patients self-manage self-monitor state access relevant information illness, which helps them cope problems allows self-treatment. Conclusions There are can apps. Additional needed efficacy side effects, including inequalities because digital divide, addictive internet behavior, lack trust mHealth, anonymity issues, risks biases social contexts, ethical implications. Further also required integrate test psychological theories improve impact adherence interventions.

Language: Английский

---

What is the Current and Future Status of Digital Mental Health Interventions?

The Spanish Journal of Psychology, Journal Year: 2022, Volume and Issue: 25

Published: Jan. 1, 2022

Abstract The prevalence of mental disorders continues to increase, especially with the advent COVID-19 pandemic. Although we have evidence-based psychological treatments address these conditions, most people encounter some barriers receiving this help (e.g., stigma, geographical or time limitations). Digital health interventions Internet-based interventions, smartphone apps, mixed realities -virtual and augmented reality) provide an opportunity improve accessibility treatments. This article summarizes main contributions different types digital solutions. It analyzes their limitations drop-out rates, lack engagement, personalization, cultural adaptations) showcases latest sophisticated innovative technological advances under umbrella precision medicine phenotyping, chatbots, conversational agents). Finally, future challenges related need for real world implementation use predictive methodology, hybrid models care in clinical practice, among others, are discussed.

Language: Английский

---

An insight into diagnosis of depression using machine learning techniques: a systematic review

Current Medical Research and Opinion, Journal Year: 2022, Volume and Issue: 38(5), P. 749 - 771

Published: Feb. 7, 2022

Background In this modern era, depression is one of the most prevalent mental disorders from which millions individuals are affected today. The symptoms heterogeneous and often coincide with other such as bipolar disorder, Parkinson's, schizophrenia, etc. It a serious illness that may lead to health problems if left untreated. Currently, identifying totally based on expertise clinician's experience. order assist clinicians in characteristics classifying depressed people, different types data modalities machine learning techniques have been incorporated by researchers field. This study aims find answers some important questions related trend publications, modality, models, dataset usage, pre-processing feature extraction selection guide direction future research diagnosis.Methods systematic review was conducted using broad range articles two major databases: IEEE Xplore PubMed. Studies ranging years 2011 April 2021 were retrieved databases resulting total 590 (53 database 537 PubMed database). Out those, satisfied defined inclusion criteria investigated for further analysis.Results A 135 identified analysed review. High growth number publications has observed recent years. Furthermore, significant diversity use classifiers also noted study. fMRI an SVM classifier found be popular choice among researchers. studies, scarcity small sample size, particularly neuroimaging concerns. identical tools similar can seen. provides statistical analysis current framework respect classifier, size accuracy applying one-way ANOVA Tukey – Kramer test.Conclusion results indicate effective fusion potential modality promising assisting automatic diagnosis.

Language: Английский

---